Building evidence-based knowledge bases from full-text literature for disease-specific biomedical reasoning
Biomedical knowledge resources often either preserve evidence as unstructured text or compress it into flat triples that omit study design, provenance, and quantitative support. Here we present EvidenceNet, a disease-specific dataset of record-level evidence collections and corresponding graph representations derived from full-text biomedical literature. EvidenceNet uses a large language model (LLM)-assisted pipeline to extract experimentally grounded findings as structured evidence records, normalize biomedical entities, score evidence quality, and connect related records through typed semantic relations. We release EvidenceNet-HCC with 7,872 evidence records and a corresponding graph wi...
| Authors | Chang Zong, Jinyu Chen, Sicheng Lv et al. |
|---|---|
| Primary category | cs.CE |
| Published | 30 March 2026 |
| Last updated at source | 5 September 2026 |
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